Node-based optimization of LoRa transmissions with Multi-Armed Bandit algorithms. - Raouf Kerkouche; Réda Alami; Raphaël Féraud; Nadège Varsier; Patrick Maillé (ICT, 2018)

Federated Learning in Adversarial Settings. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia (CoRR, 2020)

Privacy-preserving and bandwidth-efficient federated learning: an application to in-hospital mortality prediction. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia; Pierre Genevès (CHIL, 2021)

Compression Boosts Differentially Private Federated Learning. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia; Pierre Genevès (EuroS&P, 2021)

Constrained differentially private federated learning for low-bandwidth devices. - Raouf Kerkouche; Gergely Ács; Claude Castelluccia; Pierre Genevès (UAI, 2021)

Differentially Private Federated Learning for Bandwidth and Energy Constrained Environments. (Apprentissage fédéré avec confidentialité différentielle pour les environnements contraints en bande passante et énergie). - Raouf Kerkouche (2021)

Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data. - Shadi Rahimian; Raouf Kerkouche; Ina Kurth; Mario Fritz (CHIL, 2022)

Private Set Generation with Discriminative Information. - Dingfan Chen; Raouf Kerkouche; Mario Fritz (NeurIPS, 2022)

Client-specific Property Inference against Secure Aggregation in Federated Learning. - Raouf Kerkouche; Gergely Ács; Mario Fritz (WPES@CCS, 2023)

Fed-GLOSS-DP: Federated, Global Learning using Synthetic Sets with Record Level Differential Privacy. - Hui-Po Wang; Dingfan Chen; Raouf Kerkouche; Mario Fritz (CoRR, 2023)

Privacy-Aware Document Visual Question Answering. - Rubèn Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Lei Kang; Ernest Valveny; Antti Honkela; Mario Fritz; Dimosthenis Karatzas (CoRR, 2023)

Privacy-Aware Document Visual Question Answering. - Rubèn Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Joonas Jälkö; Vincent Poulain D'Andecy; Aurélie Joseph; Lei Kang; Ernest Valveny; Antti Honkela; et al. (ICDAR (6), 2024)

Private and Collaborative Kaplan-Meier Estimators. - Shadi Rahimian; Raouf Kerkouche; Ina Kurth; Mario Fritz (WPES@CCS, 2024)

Towards Biologically Plausible and Private Gene Expression Data Generation. - Dingfan Chen; Marie Oestreich; Tejumade Afonja; Raouf Kerkouche; Matthias Becker 003; Mario Fritz (CoRR, 2024)

FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations. - Hui-Po Wang; Dingfan Chen; Raouf Kerkouche; Mario Fritz (Proc. Priv. Enhancing Technol., 2024)

A Unified View of Differentially Private Deep Generative Modeling. - Dingfan Chen; Raouf Kerkouche; Mario Fritz (Trans. Mach. Learn. Res., 2024)

DocMIA: Document-Level Membership Inference Attacks against DocVQA Models. - Khanh Nguyen; Raouf Kerkouche; Mario Fritz; Dimosthenis Karatzas (ICLR, 2025)

DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators. - Tejumade Afonja; Hui-Po Wang; Raouf Kerkouche; Mario Fritz (Trans. Mach. Learn. Res., 2025)

NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. - Marlon Tobaben; Mohamed Ali Souibgui; Rubèn Tito; Khanh Nguyen; Raouf Kerkouche; Kangsoo Jung; Joonas Jälkö; Lei Kang; Andrey Barsky; Vincent Poulain D'Andecy; Aurélie Joseph; Aashiq Muhamed; et al. (Trans. Mach. Learn. Res., 2025)